{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generative-models-from-the-perspective-of","title":"Generative Models from the perspective of Continual Learning","arxiv_id":"1812.09111","date":"2018-12-21","proceeding":"ICLR 2019 5","authors":["Timothée Lesort","Hugo Caselles-Dupré","Michael Garcia-Ortiz","Andrei Stoian","David Filliat"],"abstract":"Which generative model is the most suitable for Continual Learning? This\npaper aims at evaluating and comparing generative models on disjoint sequential\nimage generation tasks. We investigate how several models learn and forget,\nconsidering various strategies: rehearsal, regularization, generative replay\nand fine-tuning. We used two quantitative metrics to estimate the generation\nquality and memory ability. We experiment with sequential tasks on three\ncommonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and\nCIFAR10). We found that among all models, the original GAN performs best and\namong Continual Learning strategies, generative replay outperforms all other\nmethods. Even if we found satisfactory combinations on MNIST and Fashion MNIST,\ntraining generative models sequentially on CIFAR10 is particularly instable,\nand remains a challenge. Our code is available online\n\\footnote{\\url{https://github.com/TLESORT/Generative\\_Continual\\_Learning}}.","url_abs":"http://arxiv.org/abs/1812.09111v1","url_pdf":"http://arxiv.org/pdf/1812.09111v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"generative-models-from-the-perspective-of","repo_url":"https://github.com/TLESORT/Generative_Continual_Learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"continual-learning","task_name":"Continual Learning"},{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.09111","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.09111"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/TLESORT/Generative_Continual_Learning","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"555d531862c73a7f","entry":"Generator","repo":"TLESORT/Generative_Continual_Learning","repo_kind":"official","path":"Generative_Models/generator.py","file_url":"https://github.com/TLESORT/Generative_Continual_Learning/blob/HEAD/Generative_Models/generator.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"555d531862c73a7f"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}